docstr: Sample all M utterances for the given speaker.
docstr_tok: ["Sample","all","M","utterances","for","the","given","speaker","."]
code: def __sample_speaker_utterances(self, speaker):
      """
      Sample all M utterances for the given speaker.
      """
      wavs = []
      labels = []
      for _ in range(self.num_utter_per_speaker):
          # TODO:dummy but works
          while True:
              if len(self.speaker_to_utters[speaker]) > 0:
                  utter = random.sample(
                      self.speaker_to_utters[speaker], 1
                  )[0]
              else:
                  self.speakers.remove(speaker)
                  speaker, _ = self.__sample_speaker()
                  continue
              wav = self.load_wav(utter)
              if wav.shape[0] - self.seq_len > 0:
                  break
              self.speaker_to_utters[speaker].remove(utter)
  
          wavs.append(wav)
          labels.append(speaker)
      return wavs, labels
  
file_path: TTS/TTS/speaker_encoder/dataset.py
imports: import numpy
  import numpy
  import queue
  import torch
  import random
  from torch.utils.data import Dataset
  from tqdm import tqdm
label_desc: Use image node_id 7 for calling the MyDataset obj's underlying member method code with example usage: obj.__sample_speaker_utterances(speaker) and returns: wavs,  labels